Using Iterative MapReduce for Parallel Virtual Screening

Ahmed, Laeeq; Edlund, Åke; Laure, Erwin; Spjuth, Ola

doi:10.1109/cloudcom.2013.99

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…In LBVS SBVS [10] Map reduce based solutions [19] LBVS SBVS [12] Hadoop based solutions [19] LBVS SBVS [12]…”

Section: 2chemical Fingerprintsmentioning

confidence: 99%

“…These libraries can be stored in different formats such as SDF or smiles files (refers to the "variety" of data) and the high rate of data generation refers to the "velocity", "Volume", "variety" and "velocity" are data characteristics that signify BD [10] The application of ML techniques to large libraries (big data) in the VS process is computationally costly [11]. There is a growing need to develop sophisticated frameworks of efficient BD analytics [12]. Some of the most commonly deployed BD analytics systems are Apache Hadoop and Apache Spark [13].…”

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confidence: 99%

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Traditional machine learning and big data analytics in virtual screening: a comparative study

Hussin¹,

Omar²,

Abdel-Mageid³

et al. 2020

IJACR

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An unprecedented development in biomedical data has been observed in latest years. The capability to analyze a large portion of this data will offer many opportunities that will in turn affect the future of health care [1]. In this age, traditional storage and processing techniques are not sufficient to meet the demand and hence, computing techniques must scale to handle the huge volume of data. The main difficulty in managing these data is the speed at which they are generated, that is, data generation is much faster than the available computer resources for data analysis.

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“…In LBVS SBVS [10] Map reduce based solutions [19] LBVS SBVS [12] Hadoop based solutions [19] LBVS SBVS [12]…”

Section: 2chemical Fingerprintsmentioning

confidence: 99%

mentioning

confidence: 99%

Traditional machine learning and big data analytics in virtual screening: a comparative study

Hussin¹,

Omar²,

Abdel-Mageid³

et al. 2020

IJACR

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“…Lately, research works concerning machine learning on Big Data in VS process is quite active. In [16] the authors used Spark and MapReduce programming model to implement SVM based virtual screening. The work showed how HDFS and Spark could be used in combination to distribute and process data in parallel, with a satisfactory scaling behavior.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning for Large Scale Virtual Screening on Apache Spark

Sid

Batouche

2018

Computational Intelligence and Its Applications

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Virtual screening (VS) is an in-silico tool for drug discovery that aims to identify the candidate drugs through computational techniques by screening large libraries of small molecules. Various ligand and structure-based virtual screening approaches have been proposed in the last decades. Machine learning (ML) techniques have been widely applied in drug discovery and development process, predominantly in ligand based virtual screening approaches. Ensemble learning is a very common paradigm in ML field, where many models are trained on the same problem's data, to combine in the end the results in one improved prediction. Applying VS to massive molecular libraries (Big Data) is computationally intensive; so the split of these data to chunks to parallelize and distribute the task became necessary. For many years, MapReduce has been successfully applied on clusters to solve the problems with very large datasets, but with some limitations. Apache Spark is an open source framework for Big Data processing, which overcomes the shortcomings of MapReduce. In this paper, we propose a new approach based on ensemble learning paradigm in Apache Spark to improve in terms of execution time and precision the large-scale virtual screening. We generate a new training dataset to evaluate our approach. The experimental results show a good predictive performance up to 92% precision with an acceptable execution time.

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